Image processing for increasing visibility of obscured patterns

ABSTRACT

Methods and systems disclosed herein relate generally to systems and methods for modifying pixel values of an image to improve the visibility of target pixel patterns. A pixel-simulation application accesses an initial image including an initial set of pixel values. The initial set of pixel values define, in an initial color space, a particular color of pixels that indicate a target pixel pattern. The pixel-simulation application generates, based on the initial set of pixel values, a simulated image including a modified set of pixel values that visually indicate another color of pixels in an intermediate color space. The pixel-simulation application generates a pixel map by identifying a difference between the initial set pixel values of the initial image and the modified set of pixel values of simulated image. The pixel-simulation application generates, for display, an output image based at least in part on the pixel map.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/942,103 filed on Jul. 29, 2020, the contents of which areincorporated herein by reference at its entirety for all purposes.

TECHNICAL FIELD

This disclosure generally relates to generating a simulated image toidentify target pixel patterns. More specifically, but not by way oflimitation, this disclosure relates to modifying pixel values of animage to improve the visibility of target pixel patterns.

BACKGROUND

An image typically includes a set of pixels, in which each pixel isrepresented by a set of pixel values. Each pixel value of the setcorresponds to a different aspect of color, depending on the color spacebeing used. For example, in a Red, Green, Blue (RGB) color-space space,the three pixel values in the set respectively denote the red, green,and blue components of the color of that pixel. In a Hue, Saturation,Lightness (HSL) color-space space, the three values in the vector denotethe hue, saturation, and brightness of the color of that pixel.

Image content often cannot be seen by users having accessibility issues(e.g., color-vision deficiency). To address this issue, textalternatives are often provided to users in addition to the images. Forexample, when icons are added as images, alternative text describing theimage content of the icons are provided with the icons. Providingalternative text can effectively convey the same information conveyed bysimpler image content, but may begin to lose effectiveness if an imageincludes complicated pixel patterns that are difficult to describe intext. In other instances, color-vision accessibility issues areaddressed by image transformation algorithms. For example, conventionaltechniques identify a set of pixels that represent colors known to beassociated with low visibility to certain users (e.g., red-green colors)and transform values of the set of pixels so as to increase colorvisibility for those users.

SUMMARY

Certain embodiments involve modifying pixel values of an image toimprove the visibility of target pixel patterns. For example, apixel-simulation application accesses an initial image including aninitial set of pixel values. The initial set of pixel values define, inan initial color space, a particular color of pixels that indicate atarget pixel pattern. The pixel-simulation application generates aduplicate image from the initial image, in which the duplicate imageincludes the initial set of pixel values. The pixel-simulationapplication converts the initial set of pixel values of the duplicateimage to an intermediate set of pixel values. The intermediate set ofpixel values define the particular color of the pixels of the duplicateimage in an intermediate color space. The pixel-simulation applicationmodifies pixel values of the intermediate set of pixel values togenerate a modified set of pixel values for the duplicate image. To doso, the pixel-simulation application modifies one or more pixel valuesfrom the intermediate set of pixel values such that the modified set ofpixels visually indicate another color of pixels in the intermediatecolor space.

The pixel-simulation application converts the modified set of pixelvalues of the duplicate image to a simulated set of pixel values. Thesimulated set of pixel values define, in the initial color space, theother color of the pixels of the duplicate image. The pixel-simulationapplication generates a pixel map by identifying a difference betweenthe initial set pixel values of the initial image and the simulated setof pixel values of duplicate image. The pixel-simulation applicationgenerates, for display, an output image based at least in part on thepixel map. The output image includes an output set of pixels values thatdefine the target pixel pattern in the other color of pixels.

These illustrative embodiments are mentioned not to limit or define thedisclosure, but to provide examples to aid understanding thereof.Additional embodiments are discussed in the Detailed Description, andfurther description is provided there.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Features, embodiments, and advantages of the present disclosure arebetter understood when the following Detailed Description is read withreference to the accompanying drawings.

FIG. 1 illustrates a computing environment for generating a simulatedimage to identify target pixel patterns in accordance with someembodiments.

FIG. 2 illustrates a process for generating a simulated image toidentify target pixel patterns in accordance with some embodiments.

FIG. 3 illustrates a set of images with target pixel patterns inaccordance with some embodiments.

FIG. 4 illustrates a process for converting image pixel values toanother color space in accordance with the embodiments.

FIG. 5 illustrates a process for modifying pixel values to generate asimulated image in accordance with the embodiments.

FIG. 6 illustrates a diagram that illustrates an example of generating apixel map to identify target pixel patterns in accordance with someembodiments.

FIG. 7 illustrates a process for enhancing visibility of a target pixelpattern in accordance with the embodiments.

FIG. 8A-C depict screenshots that show examples of a user interface thatidentifies target pixel patterns in accordance with some embodiments.

FIG. 9 depicts a computing system configured for generating a simulatedimage to identify target pixel patterns in accordance with someembodiments.

DETAILED DESCRIPTION OF THE INVENTION

Conventional image-processing systems used to improve image visibilityin accessibility tools transform pixel values that represent colorsknown to be associated with low visibility to certain users, so as toincrease color visibility for those users. In addition, conventionalsystems may provide alternative text in addition to images to providecontext for users with accessibility issues. However, these conventionaltechniques may result in conveying incomplete information (e.g., textineffectively describing complex pixel patterns) or are ineffective forimproving visual accessibility (e.g., images with modified pixels stilldoes not increase visibility of hidden pixel patterns). Accordingly,existing techniques for increasing the visibility of “hidden” pixelpatterns (i.e., pixel patterns that may not be visible for those withcolor-blindness) often fail to ensure that the pixel patterns areeffectively identified from the image.

Certain embodiments described herein can address these problems byincreasing visibility of hidden pixel patterns depicted in images. In anillustrative example, a pixel-simulation application accesses an initialimage that includes an initial set of pixel values. The initial set ofpixel values defines a particular color of pixels that indicate a targetpixel pattern. For instance, the initial image could depict an image ofshoes, and the target pattern in this initial image could be a swirledpattern that includes the orange and gray colors. The target pixelpattern could depict an obscured region of the initial image that is notvisible to users with visual disabilities. In this example, a user withprotanopia is capable of identifying some content of the initial image,but may have difficulties with identifying the swirled pattern becausethe orange color of the shoes may be perceived in a different color. Theparticular color of pixels (e.g., purple, orange-tinted reds) is definedin an initial color space, in which the initial color space is a Red,Green, Blue (RGB) color space or a Hue, Saturation, Brightness (HSB)color space.

The pixel-simulation application generates and modifies a duplicate ofthe initial image to improve the visibility of the target region tousers with a visual disability. The pixel-simulation application usesthe duplicate image to generate a pixel map that identifies the targetpixel pattern, where the pixel map identifies the target pixel patternsuch that the obscured region becomes more visible. Referring to theabove example, the pixel map can thus depict the swirled pattern of theshoes depicted in the initial image, such that the visibility of theswirled pattern is substantially increased.

The pixel-simulation application converts the initial set of pixelvalues of the duplicate image, which are defined in the initial colorspace (e.g., RGB), to an intermediate set of pixel values that definethe particular color of the pixels of the duplicate image in anintermediate color space. An example of this intermediate color space isa Long, Medium, Short (LMS) color space, in which each color channel ofthe LMS space represents one of the three types of cones of the humaneye, named for their responsivity (sensitivity) peaks at long, medium,and short wavelengths. The pixel-simulation application modifies pixelvalues of the intermediate set of pixel values to generate a modifiedset of pixel values for the duplicate image. This modification involvesupdating one or more pixel values, such as values of a color channel(e.g., L-color channel). As a result, the modified set of pixelsvisually indicate another color of pixels in the intermediate colorspace.

Referring to the above example, pixel values of the shoe image can bemodified, such that the image depicts a swirled pattern with light greenand gray colors. The light green and gray pattern of the modified imagemay depict a color pattern that would be perceived by a user withprotanopia, rather than the orange and gray pattern depicted in theinitial image. Different pixel colors can be simulated by modifying apixel of a particular color channel of the intermediate color space. Forexample, the modified pixel value is a pixel value at a Long (L) colorchannel of the LMS color space, such that the modified set of pixelsvisually indicate the other color of pixels from a protanopiaperspective. In another example, the modified pixel value is a pixelvalue at a Medium (M) color channel of the LMS color space, such thatthe modified set of pixels visually indicate the other color of pixelsfrom a deuteranopia perspective. In yet another example, the modifiedpixel value is a pixel value at a Short (S) color channel of the LMScolor space, such that the modified set of pixels visually indicate theother color of pixels from a tritanopia perspective.

The pixel-simulation application converts the modified set of pixelvalues of the duplicate image to a simulated set of pixel values thatdefine the other color of the pixels of the duplicate image. Forinstance, the simulated set of pixels could be defined in the initialcolor space (e.g., the RGB space). For example, a simulated imagedepicts the shoes with light green and gray patterns at the RGB spaceinstead of the LMS space. By reverting the modified duplicate image intothe initial color space, the pixel-simulation application can calculatethe pixel map by identifying the difference between the initial imageand the modified duplicate image. The pixel-simulation applicationgenerates the pixel map by identifying a difference between the initialset of pixel values of the initial image and the simulated set of pixelvalues of duplicate image. The pixel-simulation application can access apixel value at a location of the initial image and subtract, from theaccessed pixel value, a pixel value of the duplicate image at the samelocation in the duplicate image. The pixel-simulation application storesthe difference between the two pixel values in the pixel map.

The pixel-simulation application generates an output image, whichdepicts the target pixel pattern in a different color or set of colors,based at least in part on the pixel map. Referring back to the aboveexample, the output image is a grayscale image of the shoes thatincludes the swirled pattern in a manner that is easily identifiable byusers with protanopia. In some instances, the output image depicts thetarget pixel pattern and remaining portions of the output image at ahigher image-contrast ratio, such that visibility is substantiallyincreased for the target pixel pattern. This would be different from animage that includes a hidden pixel pattern with a lower contrast ratiobetween the pixel pattern and the remaining portion of the input image.As a result, the pixel-simulation application may generate the outputimage such that it includes target pixel pattern that is identifiable byusers with certain types of color blindness, including protanopia,deuteranopia, and tritanopia. In some instances, a contrast ratiobetween the target pixel pattern and a region in the output image isgreater than a second contrast ratio between the target pixel patternand the corresponding region in the initial image.

Certain embodiments described herein improve image-processing systems byimplementing techniques that are effective in identifying hidden pixelpatterns in the image. By generating a simulated image that simulatespixels that are perceived by a user with color-vision deficiency, thepixel-simulation application can derive a pixel map that reveals one ormore target pixel patterns that may otherwise be unidentifiable tocertain users. In some embodiments, the pixel map is derived using apixel-by-pixel transformation of the input image based on thecorresponding pixels the simulated image. The pixel-by-pixeltransformation produces an improved output image that provides moreprecise visual information as compared to conventional techniques thatsimply convert a set of colors. The improved output images can also beused to improve performance of image-recognition systems that rely onprocessing images with identifiable content. Accordingly, embodimentsherein reflect an improvement in functions of image-processing systemand digital image processing technology.

Further, the pixel-simulation application can generate various imagesthat simulate pixel patterns from various perspectives (e.g.,protanopia, deuteranopia). The techniques implemented by thepixel-simulation application enable a user to identify the target pixelpattern regardless of the type of color-vision deficiency. Accordingly,embodiments herein reflect an improvement from existing accessibilitysystems that are typically limited to providing text for images thathave reduced visibility.

Computing Environment

FIG. 1 illustrates a computing environment 100 for generating asimulated image to identify target pixel patterns in accordance withsome embodiments. The computing systems in this example include a serversystem 102 and computing systems 103 a and 103 b in communication via anetwork 106.

The server system 102 includes a processor 104 that is communicativelycoupled to a memory 108 and that executes computer-executable programinstructions and/or accesses information stored in the memory 108. Theprocessor 104 may include a microprocessor, an application-specificintegrated circuit (“ASIC”), a state machine, or other suitableprocessing device. The processor 104 can include any of a number ofcomputer processing devices, including one. Such a processor can includeor may be in communication with a computer-readable medium storinginstructions that, when executed by the processor 104, cause theprocessor to perform the steps described herein.

The server system 102 can provide access to images 114. The images 114may be stored in any suitable computer-readable medium and execute onany suitable processor. In some embodiments, the images 114 are storedin the memory 108 at the server system 102. The images 114 can beaccessed by the server system 102 from a remote location via a network106 and provided to computing systems 103 a and 103 b. The images 114may include pixel patterns that have reduced visibility to some usersand can be transmitted by the server system 102 to the computing systems103 a and 103 b. In some embodiments, the images 114 are generated bythe server system 102 or by another system (e.g., application servers).By generating a simulated image from one of the images 114, the serversystem 102 may increase visibility of pixel patterns such the the outputimage provides image content that can be easily identifed for manyusers.

The images 114 may be expressed within a color space. A color spacerefers to an abstract mathematical model for representing the colorsthat comprise an image. Color spaces help to ensure consistency in colorrepresentation across the potentially numerous different devices thatmay display a digital image. In several embodiments, each of the images114 comprises a set of pixels. According to some color spaces, eachpixel may be associated with a pixel value that is represented as atriplet of color-channel values. For example, with respect to the RGBcolor space, the color-channel values correspond to the levels of red(R), green (G), and blue (B) primary colors present in the pixel. Insome instances, multiple RGB color spaces exist depending on exactlywhich shade or hue of red, green, and blue is used as the primary colorfor R, G, and B (e.g., sRGB, Adobe RGB, ProPhoto RGB, scRGB, CIE RGB).In some embodiments, each of the color-channel values in the RGB colorspace is expressed as an integer between 0 and 255. A value of 0 maycorrespond to the darkest shade of a primary color, whereas a value of255 may correspond to the lightest shade. Thus, [0, 0, 0] corresponds toblack, and [255, 255, 255] corresponds to white.

A pixel-simulation application 115 stored in the memory 108 canconfigure the processor 104 to process images 114 to identify a targetpixel pattern. The pixel-simulation application 115 can configure theprocessor 104 to access some or all of the images 114 stored in thememory 108 or any other suitable computer-readable medium. Thepixel-simulation application 115 may provide a social media service, acloud service, or other network service providing access to the images114 as well as output images that identify target pixel patterns. Acloud service can include a collection of computing resources, includingcomputing systems and/or applications, that can be provided as an onlineservice via a data network. The collection of computing systems and/orhardware can be represented as a single service. The cloud service canprovide a digital hub for browsing, creating, sharing, and otherwiseusing electronic content using one or more applications provided via thecloud service.

The pixel-simulation application 115 accesses an initial image (e.g., animage of the images 114) including an initial set of pixel values thatdefine, in an initial color space (e.g., an RGB color space), aparticular color of pixels that indicate a target pixel pattern. Thepixel-simulation application 115 generates a duplicate image from theinitial image and converts the initial set of pixel values of theduplicate image to an intermediate set of pixel values that define theparticular color of the pixels of the duplicate image in an intermediatecolor space (e.g., an LMS color space). The pixel-simulation application115 modifies pixel values of the intermediate set of pixel values, suchthat the modified set of pixels visually indicate another color ofpixels in the intermediate color space.

The pixel-simulation application 115 reverts the modified set of pixelvalues of the duplicate image to a simulated set of pixel values thatdefine the other color of the pixels of the duplicate image. Thesimulated set of pixel values are defined in the initial color space(e.g., the RGB space), to facilitate the generation of pixel maps. Thepixel-simulation application 115 generates a pixel map by identifying adifference between the initial set pixel values of the initial image andthe simulated set of pixel values of duplicate image. Thepixel-simulation application 115 uses the pixel map, which providesdifference of pixel values on pixel-by-pixel basis, to generate anoutput image the define the target pixel pattern. The output image thusincludes target pixel patterns with increased visibility.

The server system 102 may also include a number of external or internaldevices such as input or output devices. For example, the server system102 is shown with an input/output (“I/O”) interface 112. A bus 110 canalso be included in the server system 102. The bus 110 cancommunicatively couple one or more components of the server system 102.

FIG. 1 also illustrates the pixel-simulation application 115 included inthe memory 108 of the server system 102. The pixel-simulationapplication 115 can include one or more modules. The pixel-simulationapplication 115 can configure the server system 102 to access, modify,transmit, or otherwise use the images 114 in response to input providedby the client applications 128 a and/or 128 b. For example, the clientapplication 128 a transmits a query to retrieve an image of the images114, at which the server system 102 may access the image by processingthe query through a database system.

The pixel-simulation application 115 can also configure the serversystem 102 to allow multiple computing systems 103 a and/or 103 b orother client systems to access or otherwise use some or all of theimages 114. In some embodiments, the pixel-simulation application 115stores data in the memory 108 identifying client accounts via which theclient applications 128 a and/or 128 b can access the pixel-simulationapplication 115. In other embodiments, the pixel-simulation application115 accesses data from a remote data source that identifies clientaccounts via which the client applications 128 a and/or 128 b can accessthe pixel-simulation application 115. The pixel-simulation application115 can store data describing associations between client accounts anditems of the images 114.

The server system 102 can include any suitable computing system forhosting the pixel-simulation application 115. In one embodiment, theserver system 102 may be a single computing system. In anotherembodiment, the server system 102 may be a virtual server implementedusing a number of computing systems connected in a grid or cloudcomputing topology.

Each of the computing systems 103 a and/or 103 b includes acomputer-readable medium such as processors 118 a and/or 118 b,respectively. Each of the processors 118 a and/or 118 b iscommunicatively coupled to a memory 120 a and/or 120 b, respectively.Each of the processors 118 a and/or 118 b respectively executescomputer-executable program instructions and/or accesses informationstored in the memory 120 a and/or 120 b. Each of the processors 118 aand/or 118 b may include a microprocessor, an ASIC, a state machine, orother processor. Each of the processors 118 a and/or 118 b can includeany of a number of computer processing devices, including one. Such aprocessor can include or may be in communication with acomputer-readable medium. The computer-readable medium storesinstructions that when executed by the processor, cause the processor toperform the steps described herein.

The computing systems 103 a and/or 103 b may also include a number ofexternal or internal devices such as a mouse, a CD-ROM, DVD, a keyboard,a display, audio speakers, one or more microphones, or any other inputor output devices. For example, each of the computing systems 103 aand/or 103 b is respectively shown with input/output (“I/O”) interfaces124 a, 124 b and display devices 126 a, 126 b. Buses 122 a, 122 b can berespectively included in the computing systems 103 a and/or 103 b. Eachof the buses 122 a, 122 b can communicatively couple one or morecomponents of the computing systems 103 a and/or 103 b.

FIG. 1 also illustrates the client applications 128 a and/or 128 brespectively included in the memory 120 a and/or 120 b of the computingsystems 103 a and/or 103 b. Each of the client applications 128 a and/or128 b can include one or more software modules. The client applications128 a and/or 128 b can respectively configure the processors 118 aand/or 118 b to modify or otherwise access the images 114 provided bythe server system 102 via the network 106. The client applications 128 aand/or 128 b can respectively configure the processors 118 a and/or 118b to respectively render some or all of the images 114 as well as outputimages that include unmasked pixel patterns. For example, each of theclient applications 128 a and/or 128 b is an internet browserapplication, a social media application, and the like. A network sessionfor accessing or otherwise modifying the images 114 can be establishedby the client applications 128 a and/or 128 b via the network 106between computing systems 103 a and/or 103 b and the server system 102.

In some embodiments, the computing systems 103 a and/or 103 b includeany suitable client devices for communicating via a network 106 andexecuting the client applications 128 a and/or 128 b. Non-limitingexamples of a computing device include a desktop computer, a tabletcomputer, a smart phone, or any other computing device suitable forusing electronic content. In other embodiments, the computing systems103 a and/or 103 b include server systems for providing electroniccontent items via the client applications 128 a and/or 128 b.

Process for Generating a Simulated Image to Identify Target PixelPatterns

FIG. 2 illustrates a process 200 for generating a simulated image toidentify target pixel patterns in accordance with some embodiments. Forillustrative purposes, the process 200 is described with reference tothe components illustrated in FIG. 1 , though other implementations arepossible. For example, the program code for pixel-simulation application115 of FIG. 1 , which is stored in a non-transitory computer-readablemedium, is executed by one or more processing devices to cause theserver system 102 to perform one or more operations described herein.

At step 202, a pixel-simulation application accesses an initial imageincluding an initial set of pixel values that define a particular colorof pixels that indicate a target pixel pattern. In some embodiments, thetarget pixel pattern depicts an obscured region of the initial imagewhich may not be visible to some users. For example, a user withprotanopia is capable of identifying some content of the initial image,but may have difficulties with identifying the obscured region thatincludes black and dark red pixel patterns. The particular color ofpixels (e.g., red, green, blue) is defined in an initial color space, inwhich the initial color space is a RGB color space or an HSB colorspace.

At step 204, the pixel-simulation application generates a duplicateimage from the initial image, in which the duplicate image includes theinitial set of pixel values. The duplicate image can be transformed intoanother image which can be used to generate a pixel map that identifiesthe target pixel pattern. In some embodiments, the pixel map identifiesthe target pixel pattern such that the obscured region becomes morevisible.

At step 206, the pixel-simulation application converts the initial setof pixel values of the duplicate image to an intermediate set of pixelvalues that define the particular color of the pixels of the duplicateimage in an intermediate color space. The intermediate color space canbe the LMS color space, in which each color channel of the LMS spacerepresents one of the three types of cones of the human eye, named fortheir responsivity (sensitivity) peaks at long, medium, and shortwavelengths.

In some instances, the pixel-simulation application converts the initialset of pixel values of the duplicate image to the intermediate set ofpixel values by applying a predetermined matrix configured to convertthe initial color space (e.g., RGB) to the intermediate color space(e.g., LMS). The LMS color space can be defined based on threecoordinates L, M and S, each one measuring the strength of excitation ofone of the three types of cones in the retina of the human eye. In someembodiments, the spectral response curves of the three LMS colorchannels are substantially overlapping, such that a pixel value in theLMS color space includes at least one non-zero color-channel value. Therange of perceivable colors corresponds to a volume in LMS color spacewhich is not simply a cuboid.

The pixel-simulation application may apply a first row of values in thepredetermined matrix to each pixel value of the initial set of pixelvalues to identify a first pixel value that defines a first color of anL channel of the LMS color space. The pixel-simulation application maythen apply a second row of values in the predetermined matrix to eachpixel value of the initial set of pixel values to identify a secondvalue that defines a second color of an M channel of the LMS colorspace. The pixel-simulation application may apply a third row of valuesin the predetermined matrix to each pixel value of the initial set ofpixel values to identify a third pixel value that defines a third colorfor an S channel of the LMS color space. Based on the calculated thefirst pixel value, the second pixel value, and the third pixel value,the pixel-simulation application may generate the intermediate set ofpixel values, such that the intermediate set of pixel values define theparticular color of pixels in the LMS color space.

At step 208, the pixel-simulation application modifies pixel values ofthe intermediate set of pixel values to generate a modified set of pixelvalues for the duplicate image. In some instances, the modified set ofpixel values includes modifying a pixel value of the intermediate set ofpixel values such that the modified set of pixels visually indicateanother color of pixels in the intermediate color space.

The modified pixel value can include a pixel value at a color channel(e.g., L-color channel) of the intermediate color space. Different pixelcolors can be simulated by modifying a pixel of a particular colorchannel of the intermediate color space. For example, the modified pixelvalue is a pixel value at a Long (L) color channel of the LMS colorspace, such that the modified set of pixels visually indicate the othercolor of pixels from a protanopia perspective. In another example, themodified pixel value is a pixel value at a Medium (M) color channel ofthe LMS color space, such that the modified set of pixels visuallyindicate the other color of pixels from a deuteranopia perspective. Inyet another example, the modified pixel value is a pixel value at aShort (S) color channel of the LMS color space, such that the modifiedset of pixels visually indicate the other color of pixels from atritanopia perspective.

At step 210, the pixel-simulation application converts the modified setof pixel values of the duplicate image to a simulated set of pixelvalues that define the other color of the pixels of the duplicate image.In some embodiments, the simulated set of pixels are defined in theinitial color space (e.g., the RGB space). By reverting the modifiedduplicate image into the initial color space, the pixel-simulationapplication can calculate the pixel map by identifying the differencebetween the initial image and the modified duplicate image.

At step 212, the pixel-simulation application generates the pixel map byidentifying a difference between the initial set pixel values of theinitial image and the simulated set of pixel values of duplicate image.The pixel-simulation application can access a pixel value at a locationof the initial image and subtracts it with a pixel value of theduplicate image at the same location in the duplicate image. Thepixel-simulation application can store the difference between the twopixel values in a data structure, such as the pixel map.

At step 216, the pixel-simulation application generates an output imagebased at least in part on the pixel map, wherein the output imageincluding an output set of pixels values that define the target pixelpattern in the other color of pixels. As a result, the pixel-simulationapplication may generate the output image such that it includes targetpixel pattern that is identifiable by users with certain types of colorblindness, including protanopia, deuteranopia, and tritanopia. In someinstances, a contrast ratio between the target pixel pattern and aregion in the output image is greater than a second contrast ratiobetween the target pixel pattern and the corresponding region in theinitial image. Process 200 terminates thereafter.

Images with Target Pixel Patterns

FIG. 3 illustrates a set of images 300 with target pixel patterns inaccordance with some embodiments. An image 305 depicts a pixel patternwith a number “6”, although the pixel pattern may not be easilyidentifiable. Moreover, the pixel pattern of the image 305 may havelower visibility to users with a particular type of color-visiondeficiency, including users associated with protanopia and deuteranopia.

In some embodiments, the pixel-simulation application accesses the image305 and generates simulated images 310 or 315. The simulated images 310or 315 can be generated by generating a duplicate copy of the image 305,converting the pixels of the duplicate image from an RGB color space toanother color space (e.g., the LMS color space), modifying the pixels tosimulate a pixel pattern that is identified to be perceived by aparticular color-vision deficiency perspective, and revert the modifiedpixels of the duplicate image back to the RGB color space. The simulatedimage 310 represents the pixel pattern identifiable from a protanopeperspective, in which: pure red colors are perceived to be black; purplecolors cannot be distinguished from blue colors; more orange-tinted redsmay appear as dim yellows; and orange-yellow-green shades appear as asimilar yellow hue. The simulated image 315 represents the pixel patternidentifiable from a deuteranope perspective, which affects huediscrimination similar to the protanope but without the dimming effect.

The pixel-simulation application can generate a pixel map to generate:(i) an output image 320 from the simulated image 310; or (ii) an outputimage 325 from the simulated image 315. The output image 320identifiable by the protanope may be generated by determining adifference between the image 305 and the simulated image 310. In someinstances, the output image 325 identifiable by the deuteranope isgenerated by determining a difference between the image 305 and thesimulated image 315. To determine a difference between two images, thepixel-simulation application can access pixel values of a pixel of theimage 305, subtract the pixel values with those of a corresponding pixelof the simulated image 310 (for example), and store the subtractedresult as pixel values of a corresponding pixel in the pixel map. Thepixel-simulation application may process the pixel map to generate theoutput image 320 (for example), including converting the pixel map intoa grayscale image. As a result, pixel pattern “6” in the output image320 is far more visible as compared to the same pixel pattern depictedin the image 305. Similar process can be applied between the image 305and the simulated image 315 to generate the output image 325.

Likewise, the pixel-simulation application can access an image 330,generate a simulated image 335, and generate a pixel map for the outputimage 340. In some embodiments, the image 330 includes another pixelpattern “74” that appears difficult for identification, and even more sofor image-recognition systems or users associated with tritanopia. Thesimulated image 335 represents the pixel pattern “74” identifiable froma tritanerope perspective, in which pink colors appear in shades ofgreen color and teal color appears in shades of grey color. Thepixel-simulation application can access pixel values of a pixel of theimage 330, subtract the pixel values with those of a corresponding pixelof the simulated image 335, and store the subtracted result as pixelvalues of a corresponding pixel in the pixel map. The pixel-simulationapplication may process the pixel map to generate the output image 340,such that the output image 340 depict the pixel pattern “74” that isreadily identifiable to a tritanerope as well as image-recognitionsystems.

Converting Image Pixel Values to Another Color Space

FIG. 4 illustrates a process 400 for converting image pixel values toanother color space in accordance with the embodiments. For illustrativepurposes, the process 400 is described with reference to the componentsillustrated in FIG. 1 , though other implementations are possible. Forexample, the program code for pixel-simulation application 115 of FIG. 1, which is stored in a non-transitory computer-readable medium, isexecuted by one or more processing devices to cause the server system102 to perform one or more operations described herein.

At step 402, a pixel-simulation application receives a duplicate imagederived from an initial image (e.g., the image 305 of FIG. 3 ). Inseveral instances, the duplicate image includes pixel values that areidentical with the initial image at an initial color space (e.g., theRGB space)

At step 404, the pixel-simulation application selects a pixel of theduplicate image. In some instances, the pixel-simulation applicationselects the pixel at a starting location within the duplicate image. Forexample, the starting location can be a two-dimensional locationcoordinate of (0,0) which identifies a pixel at a bottom-left part ofthe duplicate image. In other instances, the pixel-simulationapplication iterates through remaining pixels of the duplicate image toproduce the intermediate set of pixel values for all pixels in theduplicate image. In such event, the pixel-simulation applicationincrements a dimension value (e.g., x-dimension, y-dimension) of thestarting location to process a pixel at another location, such as apixel located at (1,0) location-coordinate value.

At step 406, the pixel-simulation application identifies a pixel valueof the pixel. If the pixel is defined in the three-channel color space(e.g., the RGB space, the HSL space), the pixel value may include threecolor-channel values that ranges between 0 and 255. Each of thecolor-channel values corresponds to a respective color channel (e.g., anR-color channel, a G-color channel).

At step 408, the pixel-simulation application accesses a transformationmatrix for converting the color space of the selected pixel. In someinstances, the pixel-simulation application converts the initial set ofpixel values of the duplicate image to the intermediate set of pixelvalues by applying a predetermined matrix configured to convert theinitial color space (e.g., RGB) to the intermediate color space (e.g.,LMS). In some embodiments, the predetermined matrix includes rows andcolumns of values, which is multiplied with another matrix thatrepresents the pixel values of a pixel in the image. The result valuesmay correspond to the intermediate set of values in the LSM color space.For example, the predetermined matrix includes HCBE's (Vienot's)transformation matrix algorithm:

$\begin{pmatrix}L \\M \\S\end{pmatrix} = {\begin{pmatrix}{1{7.8}824} & {4{3.5}161} & {{4.1}1935} \\{{3.4}5565} & {2{7.1}554} & {{3.8}6714} \\{{0.0}299566} & {{0.1}8309} & {{1.4}6709}\end{pmatrix}*\begin{pmatrix}R \\G \\B\end{pmatrix}}$

At step 410, the pixel-simulation application applies a first row ofvalues in the transformation matrix to color-channel values of the pixelvalue to identify a first intermediate color-channel value that definesa color of an L channel of the LMS color space.LMSpixel_(n)[0]=17.8824*RGBpixel[0]+43.5161*RGBpixel[1]+4.1194*RGBpixel[2]

At step 412, the pixel-simulation application applies a second row ofvalues in the transformation matrix to color-channel values of the pixelvalue to identify a second intermediate color-channel value that definesa color of an M channel of the LMS color space.LMSpixel_(n)[1]=3.4557*RGBpixel[0]+27.1554*RGBpixel[1]+3.8671*RGBpixel[2]

At step 414, the pixel-simulation application applies a third row ofvalues in the transformation matrix to color-channel values of the pixelvalue to identify a third intermediate color-channel value that definesa color of an S channel of the LMS color space.LMSpixel_(n)[2]=0.03*RGBpixel[0]+0.1843*RGBpixel[1]+1.4671*RGBpixel[2]

At step 416, the pixel-simulation application merges the firstintermediate color-channel value, the second intermediate color-channelvalue, and the third intermediate color-channel value into anintermediate pixel value for the pixel. The pixel-simulation applicationthen assigns the pixel to be represented by the intermediate pixel valuethat defines the color of the pixel in the LMS color space. The colorcorresponding to the intermediate pixel value can be identified based ona vector sum of the intermediate color-channel values along the spectralsurface. As such, the color represented by the intermediate pixel valuecan be a perceivable color are inside the conical solid enclosed by thespectral surface and the plane formed by the vectors represented by theintermediate color-channel values.LMSPixel_(n)=(x_(n),y_(n),Z_(n))

In some instances, the pixel-simulation application repeats the steps404 to 416 by iterating through remaining pixels of the duplicate image.Alternatively, the pixel-simulation application repeats the steps 404 to416 by iterating through remaining pixels of an image portion thatincludes the target pixel pattern. As a result, the duplicate image withan intermediate set of pixel values is thereby generated. Process 400terminates thereafter.

Modifying Image Pixel Values to Generate a Simulated Image

FIG. 5 illustrates a process 500 for modifying pixel values to generatea simulated image in accordance with the embodiments. For illustrativepurposes, the process 500 is described with reference to the componentsillustrated in FIG. 1 , though other implementations are possible. Forexample, the program code for pixel-simulation application 115 of FIG. 1, which is stored in a non-transitory computer-readable medium, isexecuted by one or more processing devices to cause the server system102 to perform one or more operations described herein.

At step 502, the pixel-simulation application accesses, from theduplicate image, a pixel that includes an intermediate pixel value. Insome embodiments, the intermediate set of pixel values are defined in anintermediate color space, such as the LMS color space. The intermediatepixel value can include a merged set of a first intermediatecolor-channel value, a second intermediate color-channel value, and athird intermediate color-channel value, each of which generated based onconverting the RGB color-channel values associated with the pixel.

The pixel-simulation application modifies the intermediate pixel valueto generate a modified pixel value for the pixel. In some instances, thepixel-simulation application modifyies at least one color-channel valueof the intermediate pixel value. The pixel-simulation applicationselects at least one color-channel value for modification, based on atype of color-vision deficiency to be simulated. As such, thepixel-simulation application may perform one or more of steps 504, 506,or 508, to generate the modified pixel value for the pixel.

At step 504, the pixel-simulation application modifies a color-channelvalue of the intermediate pixel value that corresponds to a L-colorchannel of the intermediate color space. Modifying a color-channel valueat the L-color channel results in the modified pixel value visuallyindicating the a color for the pixel be perceived by a protanope. Themodified color-channel value can be generated based on applying a set ofparameters to color-channel values of other color channels (e.g.,M-color channel, S-color channel) of the intermediate color space.LMSpixel[0]_(protanopia)=2.02344*LMSpixel[1]−2.52581*LMSpixel[2]

At step 506, the pixel-simulation application modifies a color-channelvalue of the intermediate pixel value that corresponds to a M-colorchannel of the intermediate color space. Modifying a color-channel valueat the M-color channel results in the modified pixel value visuallyindicating the a color for the pixel be perceived by a deuteranope. Themodified color-channel value can be generated based on applying a set ofparameters to color-channel values of other color channels (e.g.,L-color channel, S-color channel) of the intermediate color space.LMSpixel[1]_(deuteranopia)=0.49421*LMSpixel[0]+1.24827*LMSpixel[2]

At step 508, the pixel-simulation application modifies a color-channelvalue of the intermediate pixel value that corresponds to a S-colorchannel of the intermediate color space. Modifying a color-channel valueat the S-color channel results in the modified pixel value visuallyindicating the a color for the pixel be perceived by a tritanope. Themodified color-channel value can be generated based on applying a set ofparameters to color-channel values of other color channels (e.g.,L-color channel, M-color channel) of the intermediate color space.LMSpixel[2]_(tritanopia)=−0.395913*LMSpixel[0]+0.801109*LMSpixel[1]

At step 510, the pixel-simulation application converts the modifiedpixel value of the duplicate image to a simulated pixel value thatdefines the other color of the pixel in the initial color space (e.g.,the RGB color space). In some embodiments, the pixel-simulationapplication converts the modified pixel value to the simulated pixelvalue by applying another transformation matrix configured to revert theintermediate color space (e.g., LMS) to the initial color space (e.g.,RGB). For example, the simulated pixel value that define the pixel colorperceived by the protanope perspective is generated by calculating eachcolor-channel value of the simulated pixel value based on color-channelvalues of the modified pixel value:RGBpixel[0]_(protanopia)=0.0809*LMSpixel[0]_(protanopia)−0.1305*LMSpixel[1]+0.1167*LMSpixel[2]RGBpixel[1]_(protanopia)=−0.0102*LMSpixel[0]_(protanopia)+0.0540*LMSpixel[1]+0.1136*LMSpixel[2]RGBpixel[2]_(protanopia)=−0.0004*LMSpixel[0]_(protanopia)−0.0041*LMSpixel[1]+0.6935*LMSpixel[2]

By reverting the modified duplicate image into the initial color space,the pixel-simulation application can calculate the pixel map byidentifying the difference between the initial image and the modifiedduplicate image.

At step 512, the pixel-simulation application repeats the steps 502 to510 by iterating through remaining pixels of the duplicate image havingthe intermediate pixel values. Alternatively, the pixel-simulationapplication repeats the steps 502 to 510 by iterating through remainingpixels of an image portion that includes the target pixel pattern. As aresult, the intermediate set of pixel values of the duplicate image areconverted into the simulated set of pixel values. At step 514, thepixel-simulation application generates a simulated image that includesthe simulated set of pixel values. Process 500 terminates thereafter.

Generating Pixel Map to Identify Target Pixel Patterns

FIG. 6 illustrates a diagram 600 that illustrates an example ofgenerating a pixel map to identify target pixel patterns in accordancewith some embodiments. The diagram 600 includes an initial image 605, asimulated image 610, and a pixel map 615. In some instances, pixelvalues of the initial image 605 and the simulated image 610 are definedin the same color space (e.g., the RGB color space).

The pixel-simulation application generates the pixel map 615 byidentifying a difference between the initial set pixel values of theinitial image 605 and the simulated set of pixel values of the simulatedimage 610. In particular, the pixel-simulation application can access apixel 620 of the initial image. The pixel-simulation applicationsubtracts a pixel value of the pixel 620 with a pixel value of the pixel625 of the simulated image 625. As a result, the pixel-simulationapplication can assign the difference of pixel values as a new pixelvalue of a pixel 630 of the pixel map 615. As shown in the pixel map615, the pixel 630 includes the new pixel value that is contrasted witha remaining portion of the pixel map 615. The high contrast ratiobetween the pixel 630 and the remaining image portion increases thevisibility of the target pixel pattern of the image 615.

In some instances, the pixel-simulation application generates an outputimage based at least in part on the pixel map 615, in which the outputimage includes a set of pixels values that are configured to increasevisibility of the target pixel pattern for users with certaincolor-vision deficiencies. As a result, the output image includes targetpixel pattern that is easily identifiable by users and may be customizedbased on a type of color-vision deficiency, including protanopia,deuteranopia, and tritanopia. In some instances, a contrast ratiobetween the target pixel pattern and a region in the output image isgreater than a contrast ratio between the target pixel pattern (e.g.,pattern that includes the pixel 620) and the corresponding region in theinitial image 605. Additionally or alternatively, the output image isfurther converted into a grayscale image.

Enhancing Visibility of the Target Pixel Pattern

FIG. 7 illustrates a process 700 for enhancing visibility of a targetpixel pattern in accordance with the embodiments. For illustrativepurposes, the process 700 is described with reference to the componentsillustrated in FIG. 1 , though other implementations are possible. Forexample, the program code for pixel-simulation application 115 of FIG. 1, which is stored in a non-transitory computer-readable medium, isexecuted by one or more processing devices to cause the server system102 to perform one or more operations described herein.

At step 702, the pixel-simulation application accesses a pixel map(e.g., the pixel map 615 of FIG. 6 ). In some instances, the pixel mapidentifies the target pixel pattern such that the obscured regionbecomes more visible. As described herein, the pixel-simulationapplication generates the pixel map by identifying a difference betweenan initial set of pixel values of the initial image and the simulatedset of pixel values of duplicate image.

The pixel-simulation application identifies a set of color-channelvalues from pixels of the initial image. In several embodiments, acolor-channel value of a pixel identifies a value corresponding to aparticular color channel of the initial image. For example, thepixel-simulation application identifies, for each pixel of the initialimage, a color-channel value corresponding to a green-color channel. Toidentify the set of color-channel values, the pixel-simulationapplication initially selects a color channel. The color channel can beselected depending on a type of color-vision deficiency for which theoutput image is generated. As such, the pixel-simulation application mayperform one or more of steps 704, 706, or 708, depending on the selectedcolor channel.

At step 704, the pixel-simulation application identifies, from theinitial image, a set of color-channel values atss a first color channelof the initial color space. The first color channel can be a red-colorchannel of the RGB color space. For example, the pixel-simulationapplication identifies, for a given pixel, a first index value 77 of athree-dimensional array (194, 137, 243) that represents the pixel valueat the RGB color space. The pixel-simulation application sets the firstindex value 77 as a color-channel value that represents the pixel. Thepixel-simulation application iterates through other pixels of theinitial image to identify a set of color-channel values corresponding tothe red-color channel.

At step 706, the pixel-simulation application identifies, from theinitial image, a set of color-channel values at a second color channelof the initial color space. The second color channel can be agreen-color channel of the RGB color space. Referring to the aboveexample, the pixel-simulation application identifies, for the givenpixel, a second index value 137 of the three-dimensional array (194,137, 243) that represents the pixel value at the RGB color space. Thepixel-simulation application sets the second index value 137 as acolor-channel value that represents the pixel. The pixel-simulationapplication iterates through other pixels of the initial image toidentify a set of color-channel values corresponding to the greenchannel. In some instances, the pixel-simulation application identifiesthe set of color-channel values from the green-color channel to generatean output image that enhances visibility for users with green-reddeficiency (e.g., deuteranopia).

At step 708, the pixel-simulation application identifies, from theinitial image, a set of color-channel values at a third color channel ofthe initial color space. The third color channel can be a blue-colorchannel of the RGB color space. Referring to the above example, thepixel-simulation application identifies, for the given pixel, a thirdindex value 243 of the three-dimensional array (194, 137, 243) thatrepresents the pixel value at the RGB color space. The pixel-simulationapplication sets the third index value 243 as a color-channel value thatrepresents the pixel. The pixel-simulation application iterates throughother pixels of the initial image to generate a set of color-channelvalues corresponding to the blue-color channel. In some instances, thepixel-simulation application identifies the set of color-channel valuesfrom the blue-color channel to identify an output image that enhancesvisibility for users with blue-yellow deficiency (e.g., tritanopia).

At step 710, the pixel-simulation application modifies pixel values ofthe pixel map by applying each color-channel value of the identified setof color-channel values to a respective color-channel value of a pixelof the pixel map. For example, the pixel-simulation applicationidentifies that the set of color-channel values is associated with agreen-color channel. In response, the pixel-simulation applicationapplies the color-channel value to a second index value 42 of athree-dimensional array (89, 42, 197) that represents the pixel value ofa pixel of the pixel map. In some embodiments, the pixel-simulationapplication iterates through other pixels of the pixel map to modify thepixel values of the pixel map.

At step 712, the pixel-simulation application generates a modifiedoutput image based at least in part on the modified pixel map. Themodified output image (e.g., a modified grayscale image) depicts thetarget pixel pattern such that visibility is further enhanced for userswith a particular color-vision deficiency (e.g., protanopia,deuteranopia, tritanopia). For example, the modified output image isgenerated from applying the set of color-channel values from theblue-color channel to further enhance visibility for users withblue-yellow color deficiency (e.g., tritanopia). In another example, themodified output image is generated from applying the set ofcolor-channel values from the green-color channel to further enhancevisibility for users with red-green color deficiency (e.g.,deuteranopia). By adding additional color-channel information into theoutput image, the visibility of the target pixel pattern can besubstantially enhanced for users with a certain color-vision deficiency.Process 700 terminates thereafter.

Screenshot Examples

FIG. 8A-C depict screenshots 800A-C that show examples of a userinterface that identifies target pixel patterns in accordance with someembodiments. Referring to FIG. 8A, a user interface of a client device(e.g., the client system 103 a of FIG. 1 ) displays a screenshot 800A.The screenshot 800A includes three initial images, each of whichdisplaying shoes from different angles. The shoes depicted in theinitial images include orange and gray colors in swirled patterns. Theswirled patterns with orange and gray colors can be identified by manyusers, though the pattern visibility may decrease for users withprotanopia, tritanopia, or deuteranopia.

Referring to FIG. 8B, the user interface displays a screenshot 800B. Thescreenshot 800B includes three simulated images, each of whichdisplaying shoes from different angles. The shoes depicted in thesimulated images include light green and gray colors in swirledpatterns. As described herein, the simulated images can be generated bymodifying pixel values of the image pixels defined in the LMS colorspace, then converting the modified pixel values back into the RGB colorspace. The swirled patterns with light green and gray colors simulatecolors of the shoes that would be perceived by the users withprotanopia.

Referring to FIG. 8C, the user interface displays a screenshot 800C. Thescreenshot 800C includes three simulated images, each of whichdisplaying shoes from different angles. The shoes depicted in thesimulated images includes light green and gray colors in swirledpatterns. The swirled patterns with light green and gray colors simulatecolors of the shoes that would be perceived by the users withprotanopia. In addition to displaying the simulated images, the userinterface also provides an output image 810, which can identify pixelpatterns on at least portion of the simulated images 805C. As shown inFIG. 8C, the output image 810 substantially increase the visibility ofidentified pixel patterns to the users with protanopia. In someinstances, the pixel-simulation application generates additional sets ofsimulated images for the user interface. Each set of the additional setsof simulated images depict the identified pixel patterns (e.g., swirledpattern) in different color combinations. Thus, the additional sets ofsimulated images can substantial increase the visibility of identifiedpixel patterns to the users with a respective type of color-visiondeficiency (e.g., deuteranopia, tritanopia).

Example of a Computing Environment

Any suitable computing system or group of computing systems can be usedfor performing the operations described herein. For example, FIG. 9depicts a computing system 900 that can implement any of the computingsystems or environments discussed above. In some embodiments, thecomputing system 900 includes a processing device 902 that executes thepixel-simulation application 115, a memory that stores various datacomputed or used by the pixel-simulation application 115, an inputdevice 914 (e.g., a mouse, a stylus, a touchpad, a touchscreen, etc.),and an output device 916 that presents output to a user (e.g., a displaydevice that displays graphical content generated by the pixel-simulationapplication 115). For illustrative purposes, FIG. 9 depicts a singlecomputing system on which the pixel-simulation application 115 isexecuted, and the input device 914 and output device 916 are present.But these applications, datasets, and devices can be stored or includedacross different computing systems having devices similar to the devicesdepicted in FIG. 9 .

The example of FIG. 9 includes a processing device 902 communicativelycoupled to one or more memory devices 904. The processing device 902executes computer-executable program code stored in a memory device 904,accesses information stored in the memory device 904, or both. Examplesof the processing device 902 include a microprocessor, anapplication-specific integrated circuit (“ASIC”), a field-programmablegate array (“FPGA”), or any other suitable processing device. Theprocessing device 902 can include any number of processing devices,including a single processing device.

The memory device 904 includes any suitable non-transitory,computer-readable medium for storing data, program code, or both. Acomputer-readable medium can include any electronic, optical, magnetic,or other storage device capable of providing a processor withcomputer-readable instructions or other program code. Non-limitingexamples of a computer-readable medium include a magnetic disk, a memorychip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or othermagnetic storage, or any other medium from which a processing device canread instructions. The instructions may include processor-specificinstructions generated by a compiler or an interpreter from code writtenin any suitable computer-programming language, including, for example,C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, andActionScript.

The computing system 900 may also include a number of external orinternal devices, such as a display device 910, or other input or outputdevices. For example, the computing system 900 is shown with one or moreinput/output (“I/O”) interfaces 908. An I/O interface 908 can receiveinput from input devices or provide output to output devices. One ormore buses 906 are also included in the computing system 900. Each bus906 communicatively couples one or more components of the computingsystem 900 to each other or to an external component.

The computing system 900 executes program code that configures theprocessing device 902 to perform one or more of the operations describedherein. The program code includes, for example, code implementing thepixel-simulation application 115 or other suitable applications thatperform one or more operations described herein. The program code may beresident in the memory device 904 or any suitable computer-readablemedium and may be executed by the processing device 902 or any othersuitable processor. In some embodiments, all modules in thepixel-simulation application 115 are stored in the memory device 904, asdepicted in FIG. 9 . In additional or alternative embodiments, one ormore of these modules from the pixel-simulation application 115 arestored in different memory devices of different computing systems.

In some embodiments, the computing system 900 also includes a networkinterface device 912. The network interface device 912 includes anydevice or group of devices suitable for establishing a wired or wirelessdata connection to one or more data networks. Non-limiting examples ofthe network interface device 912 include an Ethernet network adapter, amodem, and/or the like. The computing system 900 is able to communicatewith one or more other computing devices (e.g., a computing device thatreceives inputs for pixel-simulation application 115 or displays outputsof the pixel-simulation application 115) via a data network using thenetwork interface device 912.

An input device 914 can include any device or group of devices suitablefor receiving visual, auditory, or other suitable input that controls oraffects the operations of the processing device 902. Non-limitingexamples of the input device 914 include a touchscreen, stylus, a mouse,a keyboard, a microphone, a separate mobile computing device, etc. Anoutput device 916 can include any device or group of devices suitablefor providing visual, auditory, or other suitable sensory output.Non-limiting examples of the output device 916 include a touchscreen, amonitor, a separate mobile computing device, etc.

Although FIG. 9 depicts the input device 914 and the output device 916as being local to the computing device that executes thepixel-simulation application 115, other implementations are possible.For instance, in some embodiments, one or more of the input device 914and the output device 916 include a remote client-computing device thatcommunicates with the computing system 900 via the network interfacedevice 912 using one or more data networks described herein.

General Considerations

Numerous specific details are set forth herein to provide a thoroughunderstanding of the claimed subject matter. However, those skilled inthe art will understand that the claimed subject matter may be practicedwithout these specific details. In other instances, methods,apparatuses, or systems that would be known by one of ordinary skillhave not been described in detail so as not to obscure claimed subjectmatter.

Unless specifically stated otherwise, it is appreciated that throughoutthis specification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” and “identifying” or the likerefer to actions or processes of a computing device, such as one or morecomputers or a similar electronic computing device or devices, thatmanipulate or transform data represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of thecomputing platform.

The system or systems discussed herein are not limited to any particularhardware architecture or configuration. A computing device can includeany suitable arrangement of components that provide a result conditionedon one or more inputs. Suitable computing devices include multi-purposemicroprocessor-based computer systems accessing stored software thatprograms or configures the computing system from a general purposecomputing apparatus to a specialized computing apparatus implementingone or more embodiments of the present subject matter. Any suitableprogramming, scripting, or other type of language or combinations oflanguages may be used to implement the teachings contained herein insoftware to be used in programming or configuring a computing device.

Embodiments of the methods disclosed herein may be performed in theoperation of such computing devices. The order of the blocks presentedin the examples above can be varied—for example, blocks can bere-ordered, combined, and/or broken into sub-blocks. Certain blocks orprocesses can be performed in parallel.

The use of “adapted to” or “configured to” herein is meant as open andinclusive language that does not foreclose devices adapted to orconfigured to perform additional tasks or steps. Additionally, the useof “based on” is meant to be open and inclusive, in that a process,step, calculation, or other action “based on” one or more recitedconditions or values may, in practice, be based on additional conditionsor values beyond those recited. Headings, lists, and numbering includedherein are for ease of explanation only and are not meant to belimiting.

While the present subject matter has been described in detail withrespect to specific embodiments thereof, it will be appreciated thatthose skilled in the art, upon attaining an understanding of theforegoing, may readily produce alterations to, variations of, andequivalents to such embodiments. Accordingly, it should be understoodthat the present disclosure has been presented for purposes of examplerather than limitation, and does not preclude the inclusion of suchmodifications, variations, and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

What is claimed is:
 1. A computer-implemented method for improving visibility of target pixel patterns in an image, the method comprising: accessing, by a pixel-simulation application, an image comprising an image region including an initial set of pixel values that define, in an initial color space, a particular color of pixels that indicate a target pixel pattern; converting, by the pixel-simulation application, the initial set of pixel values to a modified set of pixel values that define another color of the pixels in an intermediate color space, wherein the other color of the pixels is different from the particular color of the pixels; converting, by the pixel-simulation application, the modified set of pixel values to a simulated set of pixel values for the image region, wherein the simulated set of pixel values define, in the initial color space, the other color of the pixels; generating, by the pixel-simulation application, a pixel map of the image region by identifying a difference between the initial set of pixel values and the simulated set of pixel values; generating, for display and by the pixel-simulation application, a transformed image region of the image based at least in part on the pixel map, wherein the transformed image region includes an output set of pixels values that define the target pixel pattern in the other color; and outputting, by the pixel-simulation application, the transformed image region and the image.
 2. The computer-implemented method of claim 1, wherein: the initial color space is a Red, Green, Blue (RGB) color space or a Hue, Saturation, B rightness (HSB) color space; and the intermediate color space is a Long, Medium, Short (LMS) color space.
 3. The computer-implemented method of claim 2, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Long (L) color channel of the LMS color space.
 4. The computer-implemented method of claim 2, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Medium (M) color channel of the LMS color space.
 5. The computer-implemented method of claim 2, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Short (S) color channel of the LMS color space.
 6. The computer-implemented method of claim 1, wherein a first contrast ratio between colors of the target pixel pattern in the transformed image region is greater than a second contrast ratio between colors of the target pixel pattern in the image region.
 7. The computer-implemented method of claim 1, wherein the transformed image region is overlaid on top of the image region of the image when the image is being displayed on a user interface.
 8. A system for improving visibility of target pixel patterns in an image, the system comprising: one or more processors; and a memory having stored thereon instructions that, upon execution by the one or more processors, cause the one or more processors to: access an image comprising an image region including an initial set of pixel values that define, in an initial color space, a particular color of pixels that indicate a target pixel pattern; convert the initial set of pixel values to a modified set of pixel values that define another color of the pixels in an intermediate color space, wherein the other color of the pixels is different from the particular color of the pixels; convert the modified set of pixel values to a simulated set of pixel values for the image region, wherein the simulated set of pixel values define, in the initial color space, the other color of the pixels; generate a pixel map of the image region by identifying a difference between the initial set of pixel values and the simulated set of pixel values; generate a transformed image region of the image based at least in part on the pixel map, wherein the transformed image region includes an output set of pixels values that define the target pixel pattern in the other color; and output the transformed image region and the image.
 9. The system of claim 8, wherein: the initial color space is a Red, Green, Blue (RGB) color space or a Hue, Saturation, B rightness (HSB) color space; and the intermediate color space is a Long, Medium, Short (LMS) color space.
 10. The system of claim 9, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Long (L) color channel of the LMS color space.
 11. The system of claim 9, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Medium (M) color channel of the LMS color space.
 12. The system of claim 9, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Short (S) color channel of the LMS color space.
 13. The system of claim 8, wherein a first contrast ratio between colors of the target pixel pattern in the transformed image region is greater than a second contrast ratio between colors of the target pixel pattern in the image region.
 14. The system of claim 8, wherein the transformed image region is overlaid on top of the image region of the image when the image is being displayed on a user interface.
 15. A computer program product for improving visibility of target pixel patterns in an image, the computer program product tangibly embodied in a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform actions including: accessing an image comprising an image region including an initial set of pixel values that define, in an initial color space, a particular color of pixels that indicate a target pixel pattern; converting the initial set of pixel values to a modified set of pixel values that define another color of the pixels in an intermediate color space, wherein the other color of the pixels is different from the particular color of the pixels; converting the modified set of pixel values to a simulated set of pixel values for the image region, wherein the simulated set of pixel values define, in the initial color space, the other color of the pixels; generating a pixel map of the image region by identifying a difference between the initial set of pixel values and the simulated set of pixel values; generating a transformed image region of the image based at least in part on the pixel map, wherein the transformed image region includes an output set of pixels values that define the target pixel pattern in the other color; and outputting the transformed image region and the image.
 16. The computer program product of claim 15, wherein: the initial color space is a Red, Green, Blue (RGB) color space or a Hue, Saturation, B rightness (HSB) color space; and the intermediate color space is a Long, Medium, Short (LMS) color space.
 17. The computer program product of claim 16, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Long (L) color channel of the LMS color space.
 18. The computer program product of claim 16, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Medium (M) color channel of the LMS color space.
 19. The computer program product of claim 16, wherein a modified pixel value of the modified set of pixel values is a pixel value associated with a Short (S) color channel of the LMS color space.
 20. The computer program product of claim 15, wherein a first contrast ratio between colors of the target pixel pattern in the transformed image region is greater than a second contrast ratio between colors of the target pixel pattern in the image region. 